Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI1
growing stock (stemwood)
development stage · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
development stage	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	191	61	7	.	0	.	1004	26	7	100	332	37	0	.	61	74	643	37	69	80	61	62	54	59	0	.	281	53	421	40	118	64	31	77	97	47	199	72	36	74	18	100	320	34	265	41	37	98	326	52	4576	12
	fir	39	100	0	.	0	.	517	35	29	100	213	76	0	.	22	71	11	84	40	81	28	86	78	75	0	.	29	92	9	85	6	100	17	69	1	100	13	100	0	.	0	.	88	44	58	76	13	100	59	100	1269	22
	pine	35	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	42	100	0	.	0	.	0	.	1	100	1	100	20	71	0	.	9	100	0	.	0	.	32	100	1	100	0	.	15	100	160	39
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	56	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	24	60	0	.	0	.	121	53	0	.	0	.	249	33
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	79	0	.	0	.	21	63
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100
	beech	120	49	0	.	0	.	154	39	188	47	35	88	0	.	41	71	9	68	80	60	66	62	65	90	0	.	0	.	62	82	41	53	147	54	31	81	24	82	83	94	0	.	56	48	0	.	0	.	132	56	1332	16
	maple	15	82	0	.	0	.	60	45	40	70	3	71	0	.	29	100	0	.	20	79	1	100	6	100	0	.	7	71	42	64	4	73	3	100	23	62	30	100	22	94	0	.	30	51	4	80	0	.	21	72	361	21
	ash	36	60	0	.	0	.	77	51	43	78	5	100	0	.	71	82	9	75	36	63	43	94	52	100	0	.	0	.	11	74	0	.	0	.	17	64	0	.	1	100	0	.	8	76	34	52	0	.	16	100	459	24
	oak	36	90	0	.	0	.	0	.	12	73	0	.	0	.	0	.	0	.	12	100	20	59	22	100	0	.	0	.	0	.	3	85	15	100	0	.	0	.	14	100	0	.	53	74	-1	411	0	.	0	.	186	34
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	69	0	.	0	.	24	72	0	.	0	.	32	56
	other broadleaves	29	79	0	.	0	.	62	41	69	68	6	69	0	.	35	74	22	55	9	76	15	57	11	100	0	.	0	.	34	63	2	100	0	.	13	59	0	.	33	45	0	.	27	63	23	46	0	.	21	73	413	18
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	501	36	7	.	0	.	1875	20	387	40	594	42	0	.	259	53	790	32	271	39	276	45	286	60	0	.	317	52	580	38	208	59	233	47	183	41	274	59	221	41	18	100	614	28	540	32	50	99	590	42	9073	9
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	117	53	0	.	109	96	519	30	0	.	70	42	0	.	9	85	574	28	10	79	149	44	31	77	2	100	104	100	131	66	5	100	22	94	325	55	134	67	41	56	88	68	81	66	239	40	41	100	73	47	2873	13
	fir	43	70	0	.	0	.	233	42	0	.	10	117	0	.	0	.	27	71	22	100	3	73	25	100	0	.	0	.	18	84	0	.	16	71	23	59	6	100	24	78	0	.	16	88	34	51	1	100	1	100	503	23
	pine	30	68	0	.	0	.	11	76	6	100	11	100	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	30	58	0	.	0	.	0	.	5	65	0	.	42	71	147	30
	larch	0	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	91	59	0	.	2	100	0	.	0	.	0	.	29	100	0	.	0	.	0	.	0	.	18	57	0	.	1	100	154	35	0	.	16	100	313	27
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100
	other conifers	3	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	1	100	2	100	0	.	0	.	9	50
	beech	59	51	0	.	19	100	140	39	43	100	31	59	0	.	14	76	0	.	0	.	4	73	-1	192	0	.	0	.	65	50	11	85	44	63	7	93	7	100	72	68	4	100	2	100	7	86	0	.	31	72	561	19
	maple	41	65	0	.	0	.	11	67	7	100	1	100	0	.	15	80	17	89	0	.	4	71	0	.	0	.	2	100	15	100	0	.	11	74	17	75	0	.	2	75	0	.	2	74	1	100	0	.	1	275	146	28
	ash	37	61	0	.	0	.	9	71	10	100	2	80	0	.	1	100	4	100	0	.	3	100	0	.	0	.	0	.	17	100	0	.	15	80	1	100	2	100	19	74	0	.	14	100	4	100	9	100	3	88	150	27
	oak	12	100	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	38	100	6	100	0	.	19	96	0	.	0	.	0	.	14	54	0	.	0	.	107	44
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	68	0	.	0	.	0	.	0	.	0	.	47	53
	other broadleaves	7	85	0	.	0	.	15	51	62	96	0	.	0	.	7	100	27	46	26	100	5	54	2	100	0	.	1	100	7	80	4	100	20	67	0	.	16	64	26	35	1	110	4	79	34	48	0	.	5	46	268	27
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	349	35	0	.	129	97	942	23	128	69	141	41	0	.	46	53	763	23	57	84	172	39	56	72	2	100	107	98	282	41	68	61	136	45	373	50	213	53	230	29	93	64	121	47	494	26	51	84	171	41	5125	9
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	570	31	48	71	251	49	1528	18	64	60	502	30	2	75	329	48	994	23	235	53	374	36	109	62	121	55	356	45	561	27	211	48	198	52	380	37	268	47	59	46	340	51	1008	23	642	23	24	69	509	27	9685	7
	fir	113	52	64	77	5	100	710	22	23	62	157	48	0	.	54	83	35	75	122	52	231	61	32	68	28	61	42	64	151	40	6	100	25	90	123	41	31	91	22	100	77	57	307	29	113	52	59	91	104	42	2631	11
	pine	189	45	0	.	0	.	35	60	38	64	22	66	22	73	0	.	215	36	0	.	15	100	0	.	0	.	42	95	8	100	105	48	80	61	47	72	26	100	0	.	25	100	46	79	568	25	0	.	50	53	1532	14
	larch	43	52	0	.	0	.	61	65	43	86	9	100	0	.	0	0	261	29	0	.	2	100	0	.	0	.	0	.	18	71	37	100	3	75	0	.	12	100	211	47	0	.	21	72	345	27	0	.	22	71	1088	16
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	50	0	.	0	.	91	38
	other conifers	6	100	0	.	0	.	31	70	17	100	0	.	0	.	0	.	0	100	1	100	2	100	0	.	0	.	0	.	1	168	1	100	4	100	0	.	1	100	0	.	1	100	5	100	0	.	0	.	5	120	75	39
	beech	612	27	16	71	6	100	829	22	198	43	309	36	0	.	87	78	119	37	191	40	47	80	21	71	43	59	113	65	188	37	316	31	329	31	85	42	3	64	694	21	72	44	697	24	141	47	1	72	265	34	5386	8
	maple	111	32	7	71	1	100	94	31	39	58	16	62	0	.	65	57	26	47	16	63	24	66	0	.	18	52	14	71	122	43	7	80	59	42	20	49	25	91	62	45	42	46	240	27	31	34	9	100	156	34	1204	11
	ash	149	31	4	100	8	79	132	32	24	88	135	42	0	.	14	75	73	59	65	49	4	73	0	.	64	73	27	73	176	34	62	58	71	66	28	60	25	58	159	28	49	64	345	28	13	50	63	71	234	30	1925	10
	oak	123	39	0	.	0	.	81	39	63	76	28	77	182	43	0	.	13	123	7	100	3	100	15	100	1	100	8	100	14	89	30	57	20	58	0	.	14	56	282	23	0	100	366	30	38	47	0	.	34	70	1323	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	168	42	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	1204	15	0	.	36	100	29	60	0	.	0	.	1439	14
	other broadleaves	84	41	5	86	15	74	146	29	6	80	49	55	6	100	17	61	425	22	52	51	33	76	15	100	2	58	55	47	121	47	17	66	48	46	74	55	74	49	1328	13	42	56	250	32	349	25	10	71	105	48	3329	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2000	17	144	72	285	49	3647	13	515	32	1227	22	212	41	566	39	2373	14	690	27	737	32	192	47	278	38	657	32	1362	21	792	28	837	25	757	27	480	33	4021	9	649	32	3322	14	2316	14	166	51	1484	19	29707	4
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	940	26	399	52	487	51	4827	13	67	46	2498	21	0	.	763	38	2332	19	588	34	1385	26	629	37	179	66	518	38	1276	27	690	36	404	42	716	36	511	41	309	51	880	32	2980	16	1908	21	89	87	1243	24	26618	6
	fir	795	27	75	89	224	54	1024	28	40	56	334	40	0	.	63	54	111	63	674	32	774	27	561	35	22	71	137	48	473	33	37	100	509	28	212	44	27	65	169	70	73	54	1056	21	76	53	60	84	375	41	7901	8
	pine	105	33	0	.	0	.	148	47	192	42	0	.	0	.	0	.	603	31	104	62	61	70	104	58	0	.	0	.	16	71	151	52	131	41	52	100	109	59	140	72	74	58	134	56	303	46	0	.	355	33	2781	13
	larch	107	59	0	.	44	100	30	75	0	.	92	100	0	.	0	.	508	30	0	.	0	.	0	.	0	.	0	.	0	.	8	100	9	100	0	.	0	.	270	42	37	100	27	81	739	23	0	.	43	60	1914	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	159	44	0	.	0	.	231	34
	other conifers	12	70	1	100	2	100	1	100	0	.	0	.	0	.	1	100	0	.	0	.	59	100	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	1	100	17	86	0	.	0	.	7	94	102	60
	beech	1054	19	97	74	171	49	1629	18	521	31	786	29	3	.	261	52	186	47	976	25	542	33	174	39	122	53	324	51	816	31	437	29	1595	22	165	62	172	40	707	22	84	72	1663	18	119	46	39	98	618	31	13260	6
	maple	134	28	6	100	62	54	335	26	43	61	51	49	7	.	26	50	7	71	177	28	37	50	60	49	24	60	11	62	167	41	28	53	190	36	20	74	13	71	6	78	0	.	167	26	27	61	0	.	44	44	1643	10
	ash	250	31	7	100	82	56	134	36	21	100	103	57	1	.	9	67	0	.	190	50	50	50	0	.	49	83	2	100	259	44	37	74	91	35	23	73	113	58	42	45	10	100	214	31	27	75	13	100	104	41	1834	12
	oak	157	39	0	.	0	.	23	64	46	62	28	60	29	.	0	.	0	.	90	76	23	71	38	67	0	.	0	.	92	93	106	52	76	49	1	100	85	65	96	49	0	.	151	30	3	100	0	.	47	52	1092	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	460	25	0	.	5	100	0	.	0	.	0	.	465	25
	other broadleaves	291	49	5	100	0	.	158	36	27	41	46	53	14	.	3	100	39	72	116	40	9	56	17	47	75	80	2	100	86	52	1	100	43	44	16	71	15	82	302	31	16	82	272	36	48	53	3	100	29	56	1632	14
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3845	15	589	47	1073	36	8308	11	957	27	3938	18	54	.	1127	33	3857	16	2916	18	2938	18	1583	27	472	43	994	31	3189	19	1494	27	3049	17	1205	30	1045	29	2502	15	1175	29	6685	11	3409	16	205	71	2865	19	59473	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1853	23	801	37	1140	30	13451	9	315	56	3094	19	0	.	888	34	9121	10	1094	25	4931	16	1012	26	352	50	1216	34	3344	18	180	56	1109	27	2329	21	1581	28	1243	22	475	43	4997	14	2556	16	387	49	3996	15	61465	4
	fir	869	30	389	44	561	34	5300	12	199	55	607	38	0	.	223	56	259	34	1173	23	2128	18	792	29	226	49	217	66	1173	24	25	97	440	29	439	33	440	38	255	37	233	61	1401	22	129	54	105	49	1801	21	19385	6
	pine	461	35	17	100	0	.	555	35	266	45	349	47	3	.	0	.	572	26	213	47	0	.	0	.	0	.	0	.	176	50	18	72	229	39	52	88	217	46	124	58	0	.	138	82	265	42	0	.	641	22	4296	11
	larch	102	40	7	100	0	.	141	48	9	100	92	71	0	.	22	100	1511	17	23	72	75	100	0	.	0	.	6	100	326	46	0	.	0	.	16	100	19	100	314	28	0	.	60	62	1368	18	30	100	189	43	4312	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	253	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	99	54	0	.	0	.	352	33
	other conifers	16	100	0	.	0	.	48	69	0	.	39	100	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	84	99	0	.	1	100	0	.	0	.	0	.	0	.	82	100	0	.	0	.	5	71	277	46
	beech	1714	19	69	48	353	47	4227	13	902	26	527	35	0	.	222	49	234	34	1586	19	1238	22	461	28	286	49	435	59	1447	22	259	36	1394	20	389	32	462	29	236	41	123	63	1414	18	13	74	194	56	1962	16	20149	5
	maple	148	50	15	100	58	68	327	23	111	36	9	63	0	.	25	55	17	53	164	31	132	43	33	53	79	61	60	60	212	29	10	81	92	40	59	48	54	53	0	.	18	79	96	33	1	100	31	68	230	30	1982	10
	ash	180	37	88	97	55	58	356	37	83	63	88	56	3	.	30	118	25	100	112	37	153	30	16	76	49	60	0	.	178	33	27	94	26	89	126	50	146	44	5	83	1	100	246	41	7	93	55	100	397	26	2452	11
	oak	322	32	0	.	12	100	254	25	70	73	31	61	95	.	0	.	28	79	124	47	98	65	0	.	0	.	0	.	5	100	83	66	76	52	28	80	112	40	31	80	0	.	351	36	0	.	0	.	196	44	1916	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	83	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	453	35	0	.	0	.	0	.	0	.	0	.	495	33
	other broadleaves	163	39	16	100	5	100	196	29	54	44	8	71	2	.	32	64	19	47	91	43	59	52	59	68	17	65	3	72	86	46	0	.	131	42	60	54	37	61	64	41	30	100	53	52	5	54	6	86	151	39	1347	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5828	14	1403	32	2184	27	24856	7	2008	21	4845	17	103	.	1443	29	12079	9	4582	14	8814	12	2373	20	1011	35	1937	29	7031	14	602	35	3498	16	3499	18	3069	20	2724	15	879	36	8838	11	4444	13	808	37	9568	11	118428	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1338	31	239	74	622	44	8828	11	243	64	1494	28	0	.	825	38	7968	11	927	30	997	33	893	33	66	.	617	38	4096	19	180	61	858	34	1565	25	815	33	1397	23	655	42	1905	21	2069	22	393	59	1614	26	40601	5
	fir	298	40	25	100	115	57	3852	15	62	55	321	45	0	.	135	100	690	31	844	32	682	32	434	35	0	.	167	77	803	35	0	.	377	47	499	32	186	57	305	43	0	.	876	27	67	62	109	56	486	39	11336	8
	pine	92	87	0	.	5	100	289	42	88	100	64	73	0	.	0	.	368	46	93	61	0	.	13	100	0	.	0	.	61	77	0	.	54	67	0	.	91	46	37	81	0	.	9	100	159	86	0	.	456	39	1879	18
	larch	32	59	0	.	0	.	59	61	0	.	34	100	0	.	0	.	1584	20	0	.	0	.	19	100	0	.	0	.	23	100	9	100	0	.	23	100	0	.	544	33	0	.	34	100	1560	21	0	.	55	100	3976	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	299	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	73	0	.	0	.	341	34
	other conifers	0	.	0	.	0	.	69	92	0	.	0	.	0	.	0	.	0	.	0	.	15	96	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	1	100	0	.	0	.	10	100	95	69
	beech	849	30	0	.	123	61	3131	15	808	33	681	37	0	.	223	63	498	45	695	30	711	36	198	58	0	.	76	84	287	44	151	51	680	31	570	37	467	35	289	34	28	100	548	26	44	86	44	100	1034	26	12134	8
	maple	75	49	8	100	9	100	152	36	76	51	0	.	0	.	23	58	28	72	84	53	16	52	42	92	0	.	8	100	167	37	5	100	16	100	14	56	34	73	0	.	0	.	14	78	0	.	7	100	54	44	833	15
	ash	39	51	0	.	9	71	167	46	21	76	92	65	0	.	13	100	0	.	104	45	0	.	19	95	0	.	0	.	57	79	21	71	17	100	33	74	48	62	0	.	0	.	49	52	0	.	9	100	96	50	795	18
	oak	198	45	0	.	0	.	221	32	50	58	74	67	0	.	0	.	18	100	136	79	3	100	0	.	0	.	0	.	52	69	59	94	65	63	0	.	167	52	0	.	11	100	237	51	4	100	0	.	114	40	1410	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	580	36	0	.	0	.	0	.	0	.	0	.	606	34
	other broadleaves	100	51	0	.	0	.	200	36	39	77	49	100	0	.	0	.	21	65	59	49	8	100	16	71	0	.	7	100	13	54	0	.	25	68	35	61	43	77	26	48	6	87	30	83	87	75	1	100	75	47	841	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3020	20	273	75	883	42	16969	9	1388	28	2808	24	0	.	1219	33	11500	10	2943	20	2433	23	1635	27	66	.	875	39	5560	17	426	47	2093	26	2739	22	1851	25	3178	15	701	40	3702	17	4031	16	563	52	3993	18	74847	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	107	40	44	60	193	61	5217	11	142	48	1369	22	0	.	233	57	7562	9	220	35	822	24	1950	17	254	48	1160	24	1763	20	0	.	138	51	689	28	178	34	800	20	504	34	2610	13	4206	11	208	52	277	35	30648	4
	fir	45	58	27	76	19	100	3347	14	26	79	493	37	0	.	4	100	360	29	93	43	759	27	1244	19	41	100	165	38	524	26	0	.	268	44	165	41	52	69	189	63	90	50	1040	19	529	33	76	55	107	57	9663	7
	pine	95	52	0	.	0	.	232	38	18	71	50	55	0	.	0	.	981	19	98	50	22	100	24	74	0	.	41	84	61	76	0	.	242	36	24	100	49	53	30	97	69	60	6	100	675	20	1	100	39	69	2758	10
	larch	49	87	0	.	0	.	22	93	0	.	0	.	0	.	0	.	1785	13	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	826	18	0	.	66	54	2196	12	0	.	35	100	4981	8
	Arolla pine	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	577	22	0	.	0	.	0	.	0	.	21	100	11	100	0	.	0	.	0	.	0	.	0	.	8	100	0	.	311	22	0	.	0	.	933	15
	other conifers	0	.	0	.	0	.	15	102	0	.	19	100	0	.	0	.	24	50	0	.	0	.	1	100	0	.	0	.	2	100	0	.	4	79	0	.	3	100	0	.	0	0	0	.	6	100	0	.	19	67	95	33
	beech	503	33	40	84	97	64	1216	17	324	39	611	31	0	.	118	75	192	46	166	43	249	41	477	24	42	100	286	42	832	23	46	.	503	28	139	40	145	47	786	19	22	80	1046	19	96	42	16	100	302	34	8253	7
	maple	45	65	0	.	16	95	228	25	13	83	119	38	0	.	52	50	33	39	10	83	50	42	45	43	18	72	5	77	225	26	0	.	62	42	28	85	103	67	8	76	0	.	179	24	37	50	38	75	150	39	1462	11
	ash	215	49	0	.	53	78	198	29	26	100	171	45	0	.	14	71	18	64	113	51	51	52	30	78	0	.	36	50	186	27	0	.	56	52	5	100	150	33	23	62	0	.	199	45	49	53	70	70	115	45	1779	12
	oak	59	59	0	.	0	.	107	44	16	100	47	52	0	.	0	.	161	66	4	73	26	79	20	77	0	.	0	.	26	82	0	.	18	70	0	.	90	42	98	62	12	100	133	51	17	62	21	100	185	41	1040	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	794	28	0	.	0	.	0	.	0	.	0	.	799	28
	other broadleaves	175	46	1	100	2	100	142	24	49	49	110	50	0	.	8	79	114	24	19	44	56	50	21	57	1	100	28	48	201	29	2	.	67	63	7	54	188	42	347	24	8	74	106	32	144	50	0	0	216	33	2014	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1294	25	112	62	380	58	10727	9	614	35	2989	18	0	.	429	46	11811	7	724	28	2037	20	3813	13	357	51	1743	20	3834	15	48	.	1358	22	1057	25	958	28	3902	11	713	31	5385	11	8265	9	429	49	1446	23	64425	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5116	13	1537	26	2801	19	35374	5	839	30	9359	10	2	75	3109	18	29193	5	3144	14	8720	11	4677	12	974	27	4252	15	11593	10	1384	22	2759	17	6101	12	3685	16	3886	12	2961	17	13900	7	11884	7	1179	28	8037	10	176466	2
	fir	2201	17	580	33	924	25	14982	7	378	32	2135	18	0	.	501	38	1492	17	2968	15	4605	12	3165	13	316	38	757	28	3151	14	74	61	1653	17	1461	17	756	27	964	24	474	34	4785	10	1005	21	424	28	2935	15	52687	3
	pine	1007	21	17	100	5	100	1269	20	608	28	496	35	25	65	0	.	2740	13	512	27	140	47	142	45	0	.	83	63	323	34	285	33	756	20	175	46	531	25	331	40	169	38	364	39	1977	15	1	100	1597	16	13553	6
	larch	334	27	7	100	44	100	317	29	52	73	227	52	0	.	22	100	5827	8	23	72	78	95	19	100	0	.	6	100	396	39	71	60	12	77	40	72	32	72	2206	13	37	100	210	32	6482	8	30	100	360	31	16833	5
	Arolla pine	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	1255	16	0	.	0	.	0	.	0	.	21	100	11	100	0	.	0	.	0	.	0	.	0	.	8	100	0	.	670	17	0	.	0	.	1969	12
	other conifers	37	52	1	100	2	100	164	47	17	100	58	75	0	.	1	100	26	47	1	100	75	80	1	100	2	100	0	.	91	92	17	93	10	55	1	100	4	81	0	.	3	63	105	79	9	77	0	.	47	41	670	24
	beech	4911	10	222	39	769	27	11327	7	2985	14	2980	15	3	100	967	26	1237	22	3696	12	2857	15	1394	16	494	33	1234	28	3698	13	1260	16	4693	11	1386	20	1281	18	2866	10	333	32	5424	9	420	24	294	42	4344	11	61077	3
	maple	569	18	36	52	146	37	1208	12	330	22	199	27	7	100	234	25	128	25	472	18	265	25	187	29	139	38	106	37	951	14	54	35	433	20	181	25	258	34	101	36	60	40	727	13	101	27	84	44	656	17	7630	4
	ash	905	17	100	86	208	34	1073	17	226	33	596	23	4	80	153	47	129	40	622	21	304	23	117	52	162	42	66	41	884	18	146	37	277	26	234	31	484	23	249	21	59	55	1075	17	134	29	221	40	965	15	9394	5
	oak	907	18	0	.	12	100	685	16	257	32	223	30	306	41	0	.	220	51	374	37	176	40	94	42	1	100	8	100	191	51	320	32	277	26	29	78	487	25	522	19	23	72	1292	17	74	31	21	100	575	22	7073	6
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	259	33	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	3528	11	0	.	41	89	52	46	0	.	0	.	3883	10
	other broadleaves	849	22	28	64	23	55	920	13	305	29	268	31	21	71	102	35	667	16	373	21	186	27	141	34	96	64	95	31	548	19	26	48	334	23	204	28	373	26	2127	10	103	40	743	18	691	19	20	46	601	19	9843	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	16836	7	2528	23	4933	17	67324	4	5997	12	16541	8	369	39	5089	15	43173	4	12184	8	17407	8	9938	9	2184	21	6628	13	21838	7	3638	15	11203	9	9812	10	7891	11	16779	4	4229	15	28667	5	23499	5	2274	22	20118	7	361078	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1354498. Birmensdorf, Swiss Federal Research Institute WSL

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